In [12]:
import matplotlib.pyplot as plt
import seaborn as sns
qs_metrics_weights = {
'Academic Reputation Score': {"weight": 0.40},
'Employer Reputation Score': {"weight": 0.10},
'Faculty Student Score': {"weight": 0.20},
'Citations per Faculty Score': {"weight": 0.20},
'International Faculty Score': {"weight": 0.05},
'International Students Score': {"weight": 0.05},
}
def create_grid_layout_without_definitions(df, metrics_info, year):
# Set up the figure with subplots
fig, axes = plt.subplots(2, 3, figsize=(20, 10)) # Adjust figure size as needed
axes = axes.ravel()
palette = sns.color_palette("coolwarm", len(metrics_info))
# Plot each metric in the grid
for ax, (metric, info), color in zip(axes, metrics_info.items(), palette):
weight = info['weight']
sns.histplot(df[metric], kde=True, ax=ax, color=color, alpha=0.7, linewidth=0.5)
ax.set_title(f"{metric} ({weight*100}%)", fontsize=10)
ax.set_xlabel('Score', fontsize=9)
# Add a main title and adjust layout
plt.suptitle(f'Distribution of QS Ranking Metrics for {year}', fontsize=16)
plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust the layout
plt.show()
# Example usage with the 2022 dataset
create_grid_layout_without_definitions(df_2022, qs_metrics_weights, '2022')
create_grid_layout_without_definitions(df_2023, qs_metrics_weights, '2023')
create_grid_layout_without_definitions(df_2024, qs_metrics_weights, '2024')
